import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__a    :	Union[str, Any]         =    logging.get_logger(__name__)
logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if "xprophetnet" in prophetnet_checkpoint_path:
								__lowercase					     =				XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase				)
								__lowercase     ,	__lowercase					     =				XLMProphetNetForConditionalGeneration.from_pretrained(
								    lowercase				,     output_loading_info=lowercase				)
				else:
								__lowercase					     =				ProphetNetForConditionalGenerationOld.from_pretrained(lowercase				)
								__lowercase     ,	__lowercase					     =				ProphetNetForConditionalGeneration.from_pretrained(
								    lowercase				,     output_loading_info=lowercase				)

				__lowercase					     =				['''key_proj''', '''value_proj''', '''query_proj''']

				__lowercase					     =				{
				    '''self_attn''': '''ngram_self_attn''',
				    '''cross_attn''': '''encoder_attn''',
				    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
				    '''feed_forward_layer_norm''': '''final_layer_norm''',
				    '''feed_forward''': '''''',
				    '''intermediate''': '''fc1''',
				    '''output''': '''fc2''',
				    '''key_proj''': '''k_proj''',
				    '''query_proj''': '''q_proj''',
				    '''value_proj''': '''v_proj''',
				    '''word_embeddings''': '''embed_tokens''',
				    '''embeddings_layer_norm''': '''emb_layer_norm''',
				    '''relative_pos_embeddings''': '''relative_linear''',
				    '''ngram_embeddings''': '''ngram_input_embed''',
				    '''position_embeddings''': '''embed_positions''',
				}

				for key in loading_info["missing_keys"]:
								__lowercase					     =				key.split('''.'''				)

								if attributes[0] == "lm_head":
												__lowercase					     =				prophet
												__lowercase					     =				prophet_old
								else:
												__lowercase					     =				prophet.prophetnet
												__lowercase					     =				prophet_old.model

								__lowercase					     =				False
								for attribute in attributes:
												if attribute in mapping:
																__lowercase					     =				mapping[attribute]
																if not hasattr(lowercase				,     lowercase				) and len(lowercase				) > 0:
																				__lowercase					     =				attribute
												elif hasattr(lowercase				,     lowercase				):
																__lowercase					     =				attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																__lowercase					     =				old_model.weight
																logger.info(F"{attribute} is initialized."				)
																__lowercase					     =				True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																__lowercase					     =				old_model.bias
																logger.info(F"{attribute} is initialized"				)
																__lowercase					     =				True
																break
												elif attribute in special_keys and hasattr(lowercase				,     '''in_proj_weight'''				):
																__lowercase					     =				old_model.in_proj_weight.shape[0] // 3
																__lowercase					     =				getattr(lowercase				,     lowercase				)
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				__lowercase					     =				nn.Parameter(old_model.in_proj_weight[:embed_dim, :]				)
																				__lowercase					     =				nn.Parameter(old_model.in_proj_bias[:embed_dim]				)

																elif attribute == "key_proj":
																				__lowercase					     =				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
																				__lowercase					     =				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
																elif attribute == "value_proj":
																				__lowercase					     =				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]				)
																				__lowercase					     =				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]				)
																__lowercase					     =				True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																__lowercase					     =				nn.Parameter(old_model.embed_positions.weight[:512, :]				)
																__lowercase					     =				True
																break

												if attribute.isdigit():
																__lowercase					     =				model[int(lowercase				)]
																__lowercase					     =				old_model[int(lowercase				)]
												else:
																__lowercase					     =				getattr(lowercase				,     lowercase				)

																if old_attribute == "":
																				__lowercase					     =				old_model
																else:
																				if not hasattr(lowercase				,     lowercase				):
																								raise ValueError(F"{old_model} does not have {old_attribute}"				)
																				__lowercase					     =				getattr(lowercase				,     lowercase				)

								if not is_key_init:
												raise ValueError(F"{key} was not correctly initialized!"				)

				print(F"Saving model to {pytorch_dump_folder_path}"				)
				prophet.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[Any]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	int         =    parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)